{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiclass Support Vector Machine exercise\n",
    "\n",
    "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*\n",
    "\n",
    "In this exercise you will:\n",
    "    \n",
    "- implement a fully-vectorized **loss function** for the SVM\n",
    "- implement the fully-vectorized expression for its **analytic gradient**\n",
    "- **check your implementation** using numerical gradient\n",
    "- use a validation set to **tune the learning rate and regularization** strength\n",
    "- **optimize** the loss function with **SGD**\n",
    "- **visualize** the final learned weights\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'jupyterthemes'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-0607a65c16f7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mjupyterthemes\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mjtplot\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mjtplot\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstyle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtheme\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'onedork'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;31m# This is a bit of magic to make matplotlib figures appear inline in the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'jupyterthemes'"
     ]
    }
   ],
   "source": [
    "# Run some setup code for this notebook.\n",
    "\n",
    "import random\n",
    "import numpy as np\n",
    "from cs231n.data_utils import load_CIFAR10\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from __future__ import print_function\n",
    "from jupyterthemes import jtplot\n",
    "jtplot.style(theme='onedork')\n",
    "# This is a bit of magic to make matplotlib figures appear inline in the\n",
    "# notebook rather than in a new window.\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# Some more magic so that the notebook will reload external python modules;\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CIFAR-10 Data Loading and Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (50000, 32, 32, 3)\n",
      "Training labels shape:  (50000,)\n",
      "Test data shape:  (10000, 32, 32, 3)\n",
      "Test labels shape:  (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the raw CIFAR-10 data.\n",
    "cifar10_dir = 'cs231n/datasets/cifar-10-batches-py'\n",
    "\n",
    "# Cleaning up variables to prevent loading data multiple times (which may cause memory issue)\n",
    "try:\n",
    "   del X_train, y_train\n",
    "   del X_test, y_test\n",
    "   print('Clear previously loaded data.')\n",
    "except:\n",
    "   pass\n",
    "\n",
    "X_train, y_train, X_test, y_test = load_CIFAR10(cifar10_dir)\n",
    "\n",
    "# As a sanity check, we print out the size of the training and test data.\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Training labels shape: ', y_train.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 70 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize some examples from the dataset.\n",
    "# We show a few examples of training images from each class.\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "num_classes = len(classes)\n",
    "samples_per_class = 7\n",
    "for y, cls in enumerate(classes):\n",
    "    idxs = np.flatnonzero(y_train == y)#非零元素的位置\n",
    "    idxs = np.random.choice(idxs, samples_per_class, replace=False)\n",
    "    for i, idx in enumerate(idxs):\n",
    "        plt_idx = i * num_classes + y + 1\n",
    "        plt.subplot(samples_per_class, num_classes, plt_idx)\n",
    "        plt.imshow(X_train[idx].astype('uint8'))\n",
    "        plt.axis('off')\n",
    "        if i == 0:\n",
    "            plt.title(cls)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train data shape:  (49000, 32, 32, 3)\n",
      "Train labels shape:  (49000,)\n",
      "Validation data shape:  (1000, 32, 32, 3)\n",
      "Validation labels shape:  (1000,)\n",
      "Test data shape:  (1000, 32, 32, 3)\n",
      "Test labels shape:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train, val, and test sets. In addition we will\n",
    "# create a small development set as a subset of the training data;\n",
    "# we can use this for development so our code runs faster.\n",
    "num_training = 49000\n",
    "num_validation = 1000\n",
    "num_test = 1000\n",
    "num_dev = 500\n",
    "\n",
    "# Our validation set will be num_validation points from the original\n",
    "# training set.\n",
    "mask = range(num_training, num_training + num_validation)\n",
    "X_val = X_train[mask]\n",
    "y_val = y_train[mask]\n",
    "\n",
    "# Our training set will be the first num_train points from the original\n",
    "# training set.\n",
    "mask = range(num_training)\n",
    "X_train = X_train[mask]\n",
    "y_train = y_train[mask]\n",
    "\n",
    "# We will also make a development set, which is a small subset of\n",
    "# the training set.\n",
    "mask = np.random.choice(num_training, num_dev, replace=False)\n",
    "X_dev = X_train[mask]\n",
    "y_dev = y_train[mask]\n",
    "\n",
    "# We use the first num_test points of the original test set as our\n",
    "# test set.\n",
    "mask = range(num_test)\n",
    "X_test = X_test[mask]\n",
    "y_test = y_test[mask]\n",
    "\n",
    "print('Train data shape: ', X_train.shape)\n",
    "print('Train labels shape: ', y_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Validation labels shape: ', y_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('Test labels shape: ', y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (49000, 3072)\n",
      "Validation data shape:  (1000, 3072)\n",
      "Test data shape:  (1000, 3072)\n",
      "dev data shape:  (500, 3072)\n"
     ]
    }
   ],
   "source": [
    "# Preprocessing: reshape the image data into rows\n",
    "X_train = np.reshape(X_train, (X_train.shape[0], -1))\n",
    "X_val = np.reshape(X_val, (X_val.shape[0], -1))\n",
    "X_test = np.reshape(X_test, (X_test.shape[0], -1))\n",
    "X_dev = np.reshape(X_dev, (X_dev.shape[0], -1))\n",
    "\n",
    "# As a sanity check, print out the shapes of the data\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Validation data shape: ', X_val.shape)\n",
    "print('Test data shape: ', X_test.shape)\n",
    "print('dev data shape: ', X_dev.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[130.64189796 135.98173469 132.47391837 130.05569388 135.34804082\n",
      " 131.75402041 130.96055102 136.14328571 132.47636735 131.48467347]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Preprocessing: subtract the mean image\n",
    "# first: compute the image mean based on the training data\n",
    "mean_image = np.mean(X_train, axis=0)\n",
    "print(mean_image[:10]) # print a few of the elements\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.imshow(mean_image.reshape((32,32,3)).astype('uint8')) # visualize the mean image\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# second: subtract the mean image from train and test data\n",
    "X_train -= mean_image\n",
    "X_val -= mean_image\n",
    "X_test -= mean_image\n",
    "X_dev -= mean_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(49000, 3073) (1000, 3073) (1000, 3073) (500, 3073)\n"
     ]
    }
   ],
   "source": [
    "# third: append the bias dimension of ones (i.e. bias trick) so that our SVM\n",
    "# only has to worry about optimizing a single weight matrix W.\n",
    "X_train = np.hstack([X_train, np.ones((X_train.shape[0], 1))])#水平连接个长度为图片数的全为1数组\n",
    "X_val = np.hstack([X_val, np.ones((X_val.shape[0], 1))])\n",
    "X_test = np.hstack([X_test, np.ones((X_test.shape[0], 1))])\n",
    "X_dev = np.hstack([X_dev, np.ones((X_dev.shape[0], 1))])\n",
    "\n",
    "print(X_train.shape, X_val.shape, X_test.shape, X_dev.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM Classifier\n",
    "\n",
    "Your code for this section will all be written inside **cs231n/classifiers/linear_svm.py**. \n",
    "\n",
    "As you can see, we have prefilled the function `compute_loss_naive` which uses for loops to evaluate the multiclass SVM loss function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss: 9.249276\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the naive implementation of the loss we provided for you:\n",
    "from cs231n.classifiers.linear_svm import svm_loss_naive\n",
    "import time\n",
    "\n",
    "# generate a random SVM weight matrix of small numbers\n",
    "W = np.random.randn(3073, 10) * 0.0001 \n",
    "\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "print('loss: %f' % (loss, ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `grad` returned from the function above is right now all zero. Derive and implement the gradient for the SVM cost function and implement it inline inside the function `svm_loss_naive`. You will find it helpful to interleave your new code inside the existing function.\n",
    "\n",
    "To check that you have correctly implemented the gradient correctly, you can numerically estimate the gradient of the loss function and compare the numeric estimate to the gradient that you computed. We have provided code that does this for you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 2]\n",
      "[[1 2 2]\n",
      " [2 3 4]]\n",
      "[5 9 6]\n",
      "[4 7 9]\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'D' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-29c24c9c187b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mB\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mC\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mD\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'D' is not defined"
     ]
    }
   ],
   "source": [
    "A=np.array([[1,2,2],[2,3,4],[1,2,3]])\n",
    "B=np.sum(A,axis=1)\n",
    "C=np.sum(A,axis=0)\n",
    "print(np.argmax(A,axis=1))\n",
    "print(A[:-1,:])\n",
    "print(B)\n",
    "print(C)\n",
    "print(D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numerical: 10.900200 analytic: 10.900200, relative error: 1.332944e-11\n",
      "numerical: 13.262220 analytic: 13.262220, relative error: 1.216869e-11\n",
      "numerical: -1.221316 analytic: -1.221316, relative error: 3.001311e-10\n",
      "numerical: 20.499045 analytic: 20.499045, relative error: 9.948062e-14\n",
      "numerical: 0.212921 analytic: 0.212921, relative error: 1.192847e-09\n",
      "numerical: 5.027251 analytic: 5.027251, relative error: 7.057283e-11\n",
      "numerical: 9.692119 analytic: 9.692119, relative error: 1.147653e-11\n",
      "numerical: 12.756143 analytic: 12.756143, relative error: 4.328742e-13\n",
      "numerical: 7.229481 analytic: 7.229481, relative error: 1.333592e-13\n",
      "numerical: -14.662694 analytic: -14.662694, relative error: 1.734840e-11\n",
      "numerical: -2.225780 analytic: -2.225780, relative error: 8.061645e-11\n",
      "numerical: 0.065742 analytic: 0.065742, relative error: 3.763592e-09\n",
      "numerical: -3.631468 analytic: -3.631468, relative error: 2.348716e-11\n",
      "numerical: 21.668778 analytic: 21.668778, relative error: 9.189865e-12\n",
      "numerical: -47.261197 analytic: -47.261197, relative error: 4.750939e-12\n",
      "numerical: -4.023748 analytic: -4.023748, relative error: 5.740113e-11\n",
      "numerical: -44.756363 analytic: -44.756363, relative error: 5.666705e-12\n",
      "numerical: -17.910446 analytic: -17.910446, relative error: 3.076363e-12\n",
      "numerical: 24.811517 analytic: 24.811517, relative error: 1.603248e-11\n",
      "numerical: -6.551938 analytic: -6.551938, relative error: 5.201356e-11\n"
     ]
    }
   ],
   "source": [
    "# Once you've implemented the gradient, recompute it with the code below\n",
    "# and gradient check it with the function we provided for you\n",
    "\n",
    "# Compute the loss and its gradient at W.\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 0.0)\n",
    "\n",
    "# Numerically compute the gradient along several randomly chosen dimensions, and\n",
    "# compare them with your analytically computed gradient. The numbers should match\n",
    "# almost exactly along all dimensions.\n",
    "from cs231n.gradient_check import grad_check_sparse\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 0.0)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)\n",
    "\n",
    "# do the gradient check once again with regularization turned on\n",
    "# you didn't forget the regularization gradient did you?\n",
    "loss, grad = svm_loss_naive(W, X_dev, y_dev, 5e1)\n",
    "f = lambda w: svm_loss_naive(w, X_dev, y_dev, 5e1)[0]\n",
    "grad_numerical = grad_check_sparse(f, W, grad)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline Question 1:\n",
    "It is possible that once in a while a dimension in the gradcheck will not match exactly. What could such a discrepancy be caused by? Is it a reason for concern? What is a simple example in one dimension where a gradient check could fail? How would change the margin affect of the frequency of this happening? *Hint: the SVM loss function is not strictly speaking differentiable*\n",
    "\n",
    "**Your Answer:** *fill this in.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss: 9.249276e+00 computed in 0.519868s\n",
      "Vectorized loss: 9.249276e+00 computed in 0.143970s\n",
      "difference: -0.000000\n"
     ]
    }
   ],
   "source": [
    "# Next implement the function svm_loss_vectorized; for now only compute the loss;\n",
    "# we will implement the gradient in a moment.\n",
    "tic = time.time()\n",
    "loss_naive, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss: %e computed in %fs' % (loss_naive, toc - tic))\n",
    "\n",
    "from cs231n.classifiers.linear_svm import svm_loss_vectorized\n",
    "tic = time.time()\n",
    "loss_vectorized, _ = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))\n",
    "\n",
    "# The losses should match but your vectorized implementation should be much faster.\n",
    "print('difference: %f' % (loss_naive - loss_vectorized))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Naive loss and gradient: computed in 0.471878s\n",
      "Vectorized loss and gradient: computed in 0.012000s\n",
      "difference: 0.000000\n"
     ]
    }
   ],
   "source": [
    "# Complete the implementation of svm_loss_vectorized, and compute the gradient\n",
    "# of the loss function in a vectorized way.\n",
    "\n",
    "# The naive implementation and the vectorized implementation should match, but\n",
    "# the vectorized version should still be much faster.\n",
    "tic = time.time()\n",
    "_, grad_naive = svm_loss_naive(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Naive loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "tic = time.time()\n",
    "_, grad_vectorized = svm_loss_vectorized(W, X_dev, y_dev, 0.000005)\n",
    "toc = time.time()\n",
    "print('Vectorized loss and gradient: computed in %fs' % (toc - tic))\n",
    "\n",
    "# The loss is a single number, so it is easy to compare the values computed\n",
    "# by the two implementations. The gradient on the other hand is a matrix, so\n",
    "# we use the Frobenius norm to compare them.\n",
    "difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')\n",
    "print('difference: %f' % difference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stochastic Gradient Descent\n",
    "\n",
    "We now have vectorized and efficient expressions for the loss, the gradient and our gradient matches the numerical gradient. We are therefore ready to do SGD to minimize the loss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 790.171403\n",
      "iteration 100 / 1500: loss 289.356251\n",
      "iteration 200 / 1500: loss 108.415531\n",
      "iteration 300 / 1500: loss 42.472225\n",
      "iteration 400 / 1500: loss 18.844338\n",
      "iteration 500 / 1500: loss 10.257121\n",
      "iteration 600 / 1500: loss 7.605035\n",
      "iteration 700 / 1500: loss 5.955225\n",
      "iteration 800 / 1500: loss 5.645213\n",
      "iteration 900 / 1500: loss 5.355503\n",
      "iteration 1000 / 1500: loss 5.259877\n",
      "iteration 1100 / 1500: loss 5.422102\n",
      "iteration 1200 / 1500: loss 5.856080\n",
      "iteration 1300 / 1500: loss 5.399020\n",
      "iteration 1400 / 1500: loss 5.030447\n",
      "That took 268.587743s\n"
     ]
    }
   ],
   "source": [
    "# In the file linear_classifier.py, implement SGD in the function\n",
    "# LinearClassifier.train() and then run it with the code below.\n",
    "from cs231n.classifiers import LinearSVM\n",
    "svm = LinearSVM()\n",
    "tic = time.time()\n",
    "loss_hist = svm.train(X_train, y_train, learning_rate=1e-7, reg=2.5e4,\n",
    "                      num_iters=1500, verbose=True)\n",
    "toc = time.time()\n",
    "print('That took %fs' % (toc - tic))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# A useful debugging strategy is to plot the loss as a function of\n",
    "# iteration number:\n",
    "plt.plot(loss_hist)\n",
    "plt.xlabel('Iteration number')\n",
    "plt.ylabel('Loss value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training accuracy: 0.366633\n",
      "validation accuracy: 0.372000\n"
     ]
    }
   ],
   "source": [
    "# Write the LinearSVM.predict function and evaluate the performance on both the\n",
    "# training and validation set\n",
    "y_train_pred = svm.predict(X_train)\n",
    "print('training accuracy: %f' % (np.mean(y_train == y_train_pred), ))\n",
    "y_val_pred = svm.predict(X_val)\n",
    "print('validation accuracy: %f' % (np.mean(y_val == y_val_pred), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iteration 0 / 1500: loss 330.968140\n",
      "iteration 100 / 1500: loss 216.702395\n",
      "iteration 200 / 1500: loss 145.330331\n",
      "iteration 300 / 1500: loss 97.775739\n",
      "iteration 400 / 1500: loss 67.228205\n",
      "iteration 500 / 1500: loss 46.133970\n",
      "iteration 600 / 1500: loss 32.545734\n",
      "iteration 700 / 1500: loss 23.161584\n",
      "iteration 800 / 1500: loss 17.056171\n",
      "iteration 900 / 1500: loss 13.289246\n",
      "iteration 1000 / 1500: loss 10.120760\n",
      "iteration 1100 / 1500: loss 8.900590\n",
      "iteration 1200 / 1500: loss 7.156206\n",
      "iteration 1300 / 1500: loss 6.893014\n",
      "iteration 1400 / 1500: loss 5.798356\n",
      "iteration 0 / 1500: loss 641.692321\n",
      "iteration 100 / 1500: loss 285.547386\n",
      "iteration 200 / 1500: loss 129.269791\n",
      "iteration 300 / 1500: loss 60.123200\n",
      "iteration 400 / 1500: loss 29.593533\n",
      "iteration 500 / 1500: loss 16.064020\n",
      "iteration 600 / 1500: loss 9.325020\n",
      "iteration 700 / 1500: loss 7.401436\n",
      "iteration 800 / 1500: loss 6.168286\n",
      "iteration 900 / 1500: loss 5.604811\n",
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    },
    {
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     "text": [
      "D:\\file\\learn\\jupyter notebook\\CS231n\\spring1718_assignment1\\assignment1\\cs231n\\classifiers\\linear_svm.py:79: RuntimeWarning: overflow encountered in double_scalars\n",
      "  loss+=reg * np.sum(W * W)\n",
      "D:\\Anaconda\\Anaconda_install\\envs\\cs231n\\lib\\site-packages\\numpy\\core\\_methods.py:32: RuntimeWarning: overflow encountered in reduce\n",
      "  return umr_sum(a, axis, dtype, out, keepdims)\n",
      "D:\\file\\learn\\jupyter notebook\\CS231n\\spring1718_assignment1\\assignment1\\cs231n\\classifiers\\linear_svm.py:79: RuntimeWarning: overflow encountered in multiply\n",
      "  loss+=reg * np.sum(W * W)\n"
     ]
    },
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     "output_type": "stream",
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     "text": [
      "D:\\file\\learn\\jupyter notebook\\CS231n\\spring1718_assignment1\\assignment1\\cs231n\\classifiers\\linear_svm.py:76: RuntimeWarning: overflow encountered in subtract\n",
      "  judge=score+1-corr_sc\n",
      "D:\\file\\learn\\jupyter notebook\\CS231n\\spring1718_assignment1\\assignment1\\cs231n\\classifiers\\linear_svm.py:76: RuntimeWarning: invalid value encountered in subtract\n",
      "  judge=score+1-corr_sc\n",
      "D:\\file\\learn\\jupyter notebook\\CS231n\\spring1718_assignment1\\assignment1\\cs231n\\classifiers\\linear_svm.py:77: RuntimeWarning: invalid value encountered in less\n",
      "  judge[judge<0]=0\n",
      "D:\\file\\learn\\jupyter notebook\\CS231n\\spring1718_assignment1\\assignment1\\cs231n\\classifiers\\linear_svm.py:96: RuntimeWarning: overflow encountered in multiply\n",
      "  dW=(np.dot(X.T,judge))/X.shape[0]+reg*2*W\n",
      "D:\\file\\learn\\jupyter notebook\\CS231n\\spring1718_assignment1\\assignment1\\cs231n\\classifiers\\linear_svm.py:94: RuntimeWarning: invalid value encountered in greater\n",
      "  judge[judge>0]=1\n"
     ]
    },
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     "name": "stdout",
     "output_type": "stream",
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      "lr 1.000000e-07 reg 1.000000e+04 train accuracy: 0.385939 val accuracy: 0.388000\n",
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      "lr 5.000000e-05 reg 1.000000e+05 train accuracy: 0.100265 val accuracy: 0.087000\n",
      "best validation accuracy achieved during cross-validation: 0.395000\n"
     ]
    }
   ],
   "source": [
    "# Use the validation set to tune hyperparameters (regularization strength and\n",
    "# learning rate). You should experiment with different ranges for the learning\n",
    "# rates and regularization strengths; if you are careful you should be able to\n",
    "# get a classification accuracy of about 0.4 on the validation set.\n",
    "learning_rates = [1e-7, 2e-7, 3e-7, 5e-5, 8e-7]\n",
    "regularization_strengths = [1e4, 2e4, 3e4, 4e4, 5e4, 6e4, 7e4, 8e4, 1e5]\n",
    "\n",
    "# results is dictionary mapping tuples of the form\n",
    "# (learning_rate, regularization_strength) to tuples of the form\n",
    "# (training_accuracy, validation_accuracy). The accuracy is simply the fraction\n",
    "# of data points that are correctly classified.\n",
    "results = {}\n",
    "best_val = -1   # The highest validation accuracy that we have seen so far.\n",
    "best_svm = None # The LinearSVM object that achieved the highest validation rate.\n",
    "\n",
    "for i in range(0,5):\n",
    "    for j in range(0,9):\n",
    "        svm = LinearSVM()\n",
    "        loss_hist = svm.train(X_train, y_train, learning_rate=learning_rates[i],\\\n",
    "                              reg=regularization_strengths[j],num_iters=1500, verbose=True)\n",
    "        y_train_pred = svm.predict(X_train)\n",
    "        y_val_pred = svm.predict(X_val)\n",
    "        trn_accu=np.mean(y_train == y_train_pred)\n",
    "        val_accu=np.mean(y_val == y_val_pred)\n",
    "        results[(learning_rates[i],regularization_strengths[j])]=(trn_accu,val_accu)\n",
    "        if val_accu > best_val:\n",
    "            best_val=val_accu\n",
    "            best_svm=svm\n",
    "################################################################################\n",
    "# TODO:                                                                        #\n",
    "# Write code that chooses the best hyperparameters by tuning on the validation #\n",
    "# set. For each combination of hyperparameters, train a linear SVM on the      #\n",
    "# training set, compute its accuracy on the training and validation sets, and  #\n",
    "# store these numbers in the results dictionary. In addition, store the best   #\n",
    "# validation accuracy in best_val and the LinearSVM object that achieves this  #\n",
    "# accuracy in best_svm.                                                        #\n",
    "#                                                                              #\n",
    "# Hint: You should use a small value for num_iters as you develop your         #\n",
    "# validation code so that the SVMs don't take much time to train; once you are #\n",
    "# confident that your validation code works, you should rerun the validation   #\n",
    "# code with a larger value for num_iters.                                      #\n",
    "################################################################################\n",
    "# Your code\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################\n",
    "    \n",
    "# Print out results.\n",
    "for lr, reg in sorted(results):\n",
    "    train_accuracy, val_accuracy = results[(lr, reg)]\n",
    "    print('lr %e reg %e train accuracy: %f val accuracy: %f' % (\n",
    "                lr, reg, train_accuracy, val_accuracy))\n",
    "    \n",
    "print('best validation accuracy achieved during cross-validation: %f' % best_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the cross-validation results\n",
    "import math\n",
    "x_scatter = [math.log10(x[0]) for x in results]\n",
    "y_scatter = [math.log10(x[1]) for x in results]\n",
    "\n",
    "# plot training accuracy\n",
    "marker_size = 100\n",
    "colors = [results[x][0] for x in results]\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 training accuracy')\n",
    "\n",
    "# plot validation accuracy\n",
    "colors = [results[x][1] for x in results] # default size of markers is 20\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(x_scatter, y_scatter, marker_size, c=colors)\n",
    "plt.colorbar()\n",
    "plt.xlabel('log learning rate')\n",
    "plt.ylabel('log regularization strength')\n",
    "plt.title('CIFAR-10 validation accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "linear SVM on raw pixels final test set accuracy: 0.368000\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the best svm on test set\n",
    "y_test_pred = best_svm.predict(X_test)\n",
    "test_accuracy = np.mean(y_test == y_test_pred)\n",
    "print('linear SVM on raw pixels final test set accuracy: %f' % test_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the learned weights for each class.\n",
    "# Depending on your choice of learning rate and regularization strength, these may\n",
    "# or may not be nice to look at.\n",
    "w = best_svm.W[:-1,:] # strip out the bias\n",
    "w = w.reshape(32, 32, 3, 10)\n",
    "w_min, w_max = np.min(w), np.max(w)\n",
    "classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']\n",
    "for i in range(10):\n",
    "    plt.subplot(2, 5, i + 1)\n",
    "    #squeeze 去掉shape为1的维度\n",
    "    # Rescale the weights to be between 0 and 255\n",
    "    wimg = 255.0 * (w[:, :, :, i].squeeze() - w_min) / (w_max - w_min)\n",
    "    plt.imshow(wimg.astype('uint8'))\n",
    "    plt.axis('off')\n",
    "    plt.title(classes[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inline question 2:\n",
    "Describe what your visualized SVM weights look like, and offer a brief explanation for why they look they way that they do.\n",
    "\n",
    "**Your answer:** *fill this in*"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "toc": {
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